The aspirational vision for digital assistants is to provide a conversational interface between a human user and an electronic device. Rather than having to learn commands specific to a particular device or a particular application running on the device, a user should ideally be able to provide input or instructions in language natural to the user. The ideal digital assistant would span different devices or different applications, so that a user can complete a variety of tasks without having to engage different devices or different interfaces. Current digital assistants often fall short of these aspirations. For example, digital assistants can require training that make them useful for highly repetitive tasks, but cumbersome for new tasks the digital assistant has not previously completed or variations of previously completed tasks. As another example, digital assistants can misunderstand new commands or variations on old commands, meaning that once a “natural language” command has been established, it must still be remembered and used consistently to maximize the effectiveness of the digital assistant. As yet another example, to reduce initial training requirements, digital assistants can start from arbitrary, pre-configured patterns or assumptions that might or might not serve any particular user, and can be difficult, time-consuming or even impossible to override. Digital assistants are typically more useful in locating information, documents or applications, rather than completing tasks, whereas users are oftentimes more interested in easily completing tasks. Privacy concerns can also be of concern, as consumers would prefer to retain contextual data collected regarding their phone or application usage away from digital assistant manufacturers or other third parties. There remains a significant gap between the capabilities of available digital assistants and the aspirational function of digital assistants.